Overview

Dataset statistics

Number of variables15
Number of observations385
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory45.2 KiB
Average record size in memory120.3 B

Variable types

Numeric11
Categorical4

Warnings

RSI_14 is highly correlated with Z_30High correlation
Z_30 is highly correlated with RSI_14High correlation
Z_30 has unique values Unique
ratio_M50M180 has unique values Unique
ratio_M5M20 has unique values Unique
ratio_M20M50 has unique values Unique
ratio_MACDh_12_26_9 has unique values Unique
obv_pct_delta has unique values Unique

Reproduction

Analysis started2021-04-07 22:10:20.650937
Analysis finished2021-04-07 22:10:54.650936
Duration34 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

close
Real number (ℝ≥0)

Distinct295
Distinct (%)76.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.96348051
Minimum3.119999886
Maximum13.96000004
Zeros0
Zeros (%)0.0%
Memory size3.1 KiB
2021-04-07T17:10:54.898957image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum3.119999886
5-th percentile3.950000048
Q15.360000134
median6.800000191
Q311.56000042
95-th percentile13.18799973
Maximum13.96000004
Range10.84000015
Interquartile range (IQR)6.200000286

Descriptive statistics

Standard deviation3.26169152
Coefficient of variation (CV)0.4095811519
Kurtosis-1.426142964
Mean7.96348051
Median Absolute Deviation (MAD)2.53000021
Skewness0.3268692655
Sum3065.939996
Variance10.63863157
MonotocityNot monotonic
2021-04-07T17:10:55.209979image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.8600001344
 
1.0%
5.4899997714
 
1.0%
5.5100002294
 
1.0%
5.9499998094
 
1.0%
4.1300001144
 
1.0%
12.220000273
 
0.8%
11.300000193
 
0.8%
4.2899999623
 
0.8%
12.199999813
 
0.8%
4.2300000193
 
0.8%
Other values (285)350
90.9%
ValueCountFrequency (%)
3.1199998861
0.3%
3.240000011
0.3%
3.2899999621
0.3%
3.3499999051
0.3%
3.4500000481
0.3%
ValueCountFrequency (%)
13.960000041
0.3%
13.789999961
0.3%
13.751
0.3%
13.689999581
0.3%
13.680000311
0.3%

RSI_14
Real number (ℝ≥0)

HIGH CORRELATION

Distinct383
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.65873692
Minimum9.134719431
Maximum79.47982393
Zeros0
Zeros (%)0.0%
Memory size3.1 KiB
2021-04-07T17:10:55.489002image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum9.134719431
5-th percentile23.90651744
Q141.60272476
median50.25738194
Q359.38810147
95-th percentile70.63231609
Maximum79.47982393
Range70.3451045
Interquartile range (IQR)17.78537672

Descriptive statistics

Standard deviation13.96978126
Coefficient of variation (CV)0.2813156782
Kurtosis-0.2879144906
Mean49.65873692
Median Absolute Deviation (MAD)9.114571683
Skewness-0.2639321034
Sum19118.61372
Variance195.1547883
MonotocityNot monotonic
2021-04-07T17:10:55.768023image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
66.292947392
 
0.5%
44.701907112
 
0.5%
43.157082121
 
0.3%
49.996531911
 
0.3%
27.852729351
 
0.3%
48.277473981
 
0.3%
42.062568261
 
0.3%
55.81531361
 
0.3%
64.567962551
 
0.3%
66.393556861
 
0.3%
Other values (373)373
96.9%
ValueCountFrequency (%)
9.1347194311
0.3%
14.966066271
0.3%
15.963643361
0.3%
16.790657051
0.3%
17.175153061
0.3%
ValueCountFrequency (%)
79.479823931
0.3%
78.970851761
0.3%
77.95000971
0.3%
77.376235831
0.3%
77.347545021
0.3%

INC_2
Categorical

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
0
199 
1
186 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters385
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0
ValueCountFrequency (%)
0199
51.7%
1186
48.3%
2021-04-07T17:10:56.309064image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-04-07T17:10:56.684092image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
0199
51.7%
1186
48.3%

Most occurring characters

ValueCountFrequency (%)
0199
51.7%
1186
48.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number385
100.0%

Most frequent character per category

ValueCountFrequency (%)
0199
51.7%
1186
48.3%

Most occurring scripts

ValueCountFrequency (%)
Common385
100.0%

Most frequent character per script

ValueCountFrequency (%)
0199
51.7%
1186
48.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII385
100.0%

Most frequent character per block

ValueCountFrequency (%)
0199
51.7%
1186
48.3%

ROC_2
Real number (ℝ)

Distinct382
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2336577655
Minimum-53.46153813
Maximum35.31300609
Zeros3
Zeros (%)0.8%
Memory size3.1 KiB
2021-04-07T17:10:56.934112image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-53.46153813
5-th percentile-7.942482031
Q1-3.288359198
median-0.1731641336
Q33.513769183
95-th percentile11.51417159
Maximum35.31300609
Range88.77454422
Interquartile range (IQR)6.802128381

Descriptive statistics

Standard deviation7.142508942
Coefficient of variation (CV)30.5682498
Kurtosis11.2793244
Mean0.2336577655
Median Absolute Deviation (MAD)3.384846909
Skewness-0.775104298
Sum89.95823971
Variance51.01543399
MonotocityNot monotonic
2021-04-07T17:10:57.223136image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03
 
0.8%
-0.36166329422
 
0.5%
-1.8992530861
 
0.3%
-4.5302009931
 
0.3%
4.2387521911
 
0.3%
-3.0952296071
 
0.3%
-2.7538700341
 
0.3%
-2.3848681371
 
0.3%
5.3011996421
 
0.3%
4.2500019071
 
0.3%
Other values (372)372
96.6%
ValueCountFrequency (%)
-53.461538131
0.3%
-35.578328781
0.3%
-20.088306451
0.3%
-18.276757791
0.3%
-16.494847581
0.3%
ValueCountFrequency (%)
35.313006091
0.3%
23.025216911
0.3%
22.760291311
0.3%
22.645293911
0.3%
20.618557111
0.3%

PSL_3
Categorical

Distinct4
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
66.66666666666667
158 
33.333333333333336
150 
100.0
41 
0.0
36 

Length

Max length18
Median length17
Mean length14.8025974
Min length3

Characters and Unicode

Total characters5699
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row66.66666666666667
2nd row33.333333333333336
3rd row33.333333333333336
4th row100.0
5th row33.333333333333336
ValueCountFrequency (%)
66.66666666666667158
41.0%
33.333333333333336150
39.0%
100.041
 
10.6%
0.036
 
9.4%
2021-04-07T17:10:57.797178image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-04-07T17:10:58.043195image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
66.66666666666667158
41.0%
33.333333333333336150
39.0%
100.041
 
10.6%
0.036
 
9.4%

Most occurring characters

ValueCountFrequency (%)
62520
44.2%
32400
42.1%
.385
 
6.8%
0195
 
3.4%
7158
 
2.8%
141
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number5314
93.2%
Other Punctuation385
 
6.8%

Most frequent character per category

ValueCountFrequency (%)
62520
47.4%
32400
45.2%
0195
 
3.7%
7158
 
3.0%
141
 
0.8%
ValueCountFrequency (%)
.385
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common5699
100.0%

Most frequent character per script

ValueCountFrequency (%)
62520
44.2%
32400
42.1%
.385
 
6.8%
0195
 
3.4%
7158
 
2.8%
141
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII5699
100.0%

Most frequent character per block

ValueCountFrequency (%)
62520
44.2%
32400
42.1%
.385
 
6.8%
0195
 
3.4%
7158
 
2.8%
141
 
0.7%

CDL_DOJI_3_0.1
Categorical

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
0.0
341 
1.0
44 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1155
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.0341
88.6%
1.044
 
11.4%
2021-04-07T17:10:58.564237image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-04-07T17:10:58.791253image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0341
88.6%
1.044
 
11.4%

Most occurring characters

ValueCountFrequency (%)
0726
62.9%
.385
33.3%
144
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number770
66.7%
Other Punctuation385
33.3%

Most frequent character per category

ValueCountFrequency (%)
0726
94.3%
144
 
5.7%
ValueCountFrequency (%)
.385
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1155
100.0%

Most frequent character per script

ValueCountFrequency (%)
0726
62.9%
.385
33.3%
144
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII1155
100.0%

Most frequent character per block

ValueCountFrequency (%)
0726
62.9%
.385
33.3%
144
 
3.8%

TRUERANGE_1
Real number (ℝ≥0)

Distinct164
Distinct (%)42.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4360779211
Minimum0.1199998856
Maximum3.50999999
Zeros0
Zeros (%)0.0%
Memory size3.1 KiB
2021-04-07T17:10:59.040273image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.1199998856
5-th percentile0.1720000267
Q10.279999733
median0.3699998856
Q30.5199999809
95-th percentile0.9280002594
Maximum3.50999999
Range3.390000105
Interquartile range (IQR)0.240000248

Descriptive statistics

Standard deviation0.275968825
Coefficient of variation (CV)0.6328429201
Kurtosis41.07525982
Mean0.4360779211
Median Absolute Deviation (MAD)0.1100006104
Skewness4.572911757
Sum167.8899996
Variance0.07615879237
MonotocityNot monotonic
2021-04-07T17:10:59.322294image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2515
 
3.9%
0.340000152611
 
2.9%
0.329999923711
 
2.9%
0.289999961910
 
2.6%
0.369999885610
 
2.6%
0.57999992377
 
1.8%
0.38000011447
 
1.8%
0.36000013357
 
1.8%
0.21000003816
 
1.6%
0.26000022896
 
1.6%
Other values (154)295
76.6%
ValueCountFrequency (%)
0.11999988561
0.3%
0.12999963761
0.3%
0.13999986652
0.5%
0.14000034331
0.3%
0.14999961852
0.5%
ValueCountFrequency (%)
3.509999991
0.3%
1.6500005721
0.3%
1.4300003051
0.3%
1.3499999051
0.3%
1.2299995421
0.3%

Z_30
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct385
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1315156295
Minimum-3.188764919
Maximum4.153555278
Zeros0
Zeros (%)0.0%
Memory size3.1 KiB
2021-04-07T17:10:59.611316image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-3.188764919
5-th percentile-2.07999551
Q1-1.006289837
median0.03776937447
Q31.319476749
95-th percentile2.283475121
Maximum4.153555278
Range7.342320197
Interquartile range (IQR)2.325766586

Descriptive statistics

Standard deviation1.428093747
Coefficient of variation (CV)10.85873787
Kurtosis-0.7728615472
Mean0.1315156295
Median Absolute Deviation (MAD)1.169672639
Skewness0.09439040849
Sum50.63351737
Variance2.039451751
MonotocityNot monotonic
2021-04-07T17:10:59.895338image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.601849231
 
0.3%
-2.1665929181
 
0.3%
0.037769374471
 
0.3%
1.7256510591
 
0.3%
1.419138641
 
0.3%
-1.7530641421
 
0.3%
-2.4082822361
 
0.3%
1.3194767491
 
0.3%
2.6385749991
 
0.3%
1.9273185671
 
0.3%
Other values (375)375
97.4%
ValueCountFrequency (%)
-3.1887649191
0.3%
-2.8399971371
0.3%
-2.8242857461
0.3%
-2.7836534941
0.3%
-2.499115541
0.3%
ValueCountFrequency (%)
4.1535552781
0.3%
3.9826359791
0.3%
3.4548709851
0.3%
3.1521800411
0.3%
2.952724561
0.3%

ratio_M50M180
Real number (ℝ)

UNIQUE

Distinct385
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.02651537
Minimum-16.74797861
Maximum22.39522982
Zeros0
Zeros (%)0.0%
Memory size3.1 KiB
2021-04-07T17:11:00.186361image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-16.74797861
5-th percentile0.7587098047
Q10.958966075
median0.9815677069
Q31.043749085
95-th percentile1.328689705
Maximum22.39522982
Range39.14320843
Interquartile range (IQR)0.08478301001

Descriptive statistics

Standard deviation1.519730551
Coefficient of variation (CV)1.480475203
Kurtosis152.6709684
Mean1.02651537
Median Absolute Deviation (MAD)0.03670248411
Skewness3.53115113
Sum395.2084175
Variance2.309580946
MonotocityNot monotonic
2021-04-07T17:11:00.465381image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.1845648381
 
0.3%
1.0736762991
 
0.3%
0.96433824171
 
0.3%
1.0208237191
 
0.3%
0.95839673131
 
0.3%
0.97399137451
 
0.3%
0.96721392181
 
0.3%
1.0282438681
 
0.3%
0.95674996271
 
0.3%
1.2377928451
 
0.3%
Other values (375)375
97.4%
ValueCountFrequency (%)
-16.747978611
0.3%
-2.1619750731
0.3%
-1.7333340451
0.3%
-0.12533994591
0.3%
-0.090359621351
0.3%
ValueCountFrequency (%)
22.395229821
0.3%
9.6227767521
0.3%
2.7797719881
0.3%
2.6134029351
0.3%
2.4189053591
0.3%

ratio_M5M20
Real number (ℝ)

UNIQUE

Distinct385
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4380264844
Minimum-124.6648044
Maximum52.38880441
Zeros0
Zeros (%)0.0%
Memory size3.1 KiB
2021-04-07T17:11:00.908416image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-124.6648044
5-th percentile-1.419148957
Q10.6043954144
median0.9207721034
Q31.280042848
95-th percentile2.673113375
Maximum52.38880441
Range177.0536088
Interquartile range (IQR)0.6756474334

Descriptive statistics

Standard deviation8.593241403
Coefficient of variation (CV)19.61808637
Kurtosis150.707663
Mean0.4380264844
Median Absolute Deviation (MAD)0.3431505316
Skewness-10.46623944
Sum168.6401965
Variance73.84379781
MonotocityNot monotonic
2021-04-07T17:11:01.200439image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.3070722061
 
0.3%
0.4980806391
 
0.3%
1.0691238371
 
0.3%
1.2024290961
 
0.3%
0.84691562731
 
0.3%
1.0495866181
 
0.3%
0.37125754661
 
0.3%
0.23026311611
 
0.3%
1.1785406531
 
0.3%
1.5349306971
 
0.3%
Other values (375)375
97.4%
ValueCountFrequency (%)
-124.66480441
0.3%
-89.125092391
0.3%
-18.999961211
0.3%
-10.500003971
0.3%
-9.4651209731
0.3%
ValueCountFrequency (%)
52.388804411
0.3%
18.727091451
0.3%
12.249988081
0.3%
11.10907681
0.3%
8.487181181
0.3%

ratio_M20M50
Real number (ℝ)

UNIQUE

Distinct385
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.047206269
Minimum-30.19174175
Maximum56.79845366
Zeros0
Zeros (%)0.0%
Memory size3.1 KiB
2021-04-07T17:11:01.504463image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-30.19174175
5-th percentile0.3928716112
Q10.8843672559
median0.9872371495
Q31.143810258
95-th percentile1.828984677
Maximum56.79845366
Range86.99019541
Interquartile range (IQR)0.2594430024

Descriptive statistics

Standard deviation3.510922148
Coefficient of variation (CV)3.352655778
Kurtosis185.5388341
Mean1.047206269
Median Absolute Deviation (MAD)0.1278361263
Skewness8.05821692
Sum403.1744136
Variance12.32657433
MonotocityNot monotonic
2021-04-07T17:11:01.784482image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.1668918161
 
0.3%
0.91435297521
 
0.3%
0.75501404571
 
0.3%
1.0058834731
 
0.3%
1.0173157811
 
0.3%
0.32172471361
 
0.3%
0.997232181
 
0.3%
1.0248160551
 
0.3%
1.1334219741
 
0.3%
1.0062168541
 
0.3%
Other values (375)375
97.4%
ValueCountFrequency (%)
-30.191741751
0.3%
-20.475397561
0.3%
-2.4598805391
0.3%
-2.3427299031
0.3%
-2.060616721
0.3%
ValueCountFrequency (%)
56.798453661
0.3%
8.1018976411
0.3%
5.2307696541
0.3%
5.1823956221
0.3%
2.9926853071
0.3%

ratio_MACDh_12_26_9
Real number (ℝ)

UNIQUE

Distinct385
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9980947561
Minimum-23.11943278
Maximum10.69510419
Zeros0
Zeros (%)0.0%
Memory size3.1 KiB
2021-04-07T17:11:02.066503image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-23.11943278
5-th percentile-0.7818845584
Q10.6099323392
median0.9415147938
Q31.302839601
95-th percentile3.569944536
Maximum10.69510419
Range33.81453697
Interquartile range (IQR)0.6929072617

Descriptive statistics

Standard deviation2.16285313
Coefficient of variation (CV)2.166981759
Kurtosis46.95270609
Mean0.9980947561
Median Absolute Deviation (MAD)0.3458909596
Skewness-3.557846664
Sum384.2664811
Variance4.677933662
MonotocityNot monotonic
2021-04-07T17:11:02.346525image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.4798616951
 
0.3%
1.5362289541
 
0.3%
2.4639424291
 
0.3%
0.81252362211
 
0.3%
0.41550514971
 
0.3%
1.0086028691
 
0.3%
0.18295098121
 
0.3%
0.71163362681
 
0.3%
1.014436151
 
0.3%
1.544381821
 
0.3%
Other values (375)375
97.4%
ValueCountFrequency (%)
-23.119432781
0.3%
-12.007712821
0.3%
-8.2645344371
0.3%
-6.3803130331
0.3%
-4.3497912861
0.3%
ValueCountFrequency (%)
10.695104191
0.3%
10.610850271
0.3%
9.1698675671
0.3%
8.8017702731
0.3%
8.6111671891
0.3%

obv_pct_delta
Real number (ℝ)

UNIQUE

Distinct385
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.04887046669
Minimum-112.3494949
Maximum61.27477134
Zeros0
Zeros (%)0.0%
Memory size3.1 KiB
2021-04-07T17:11:02.627547image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-112.3494949
5-th percentile-1.774690507
Q1-0.2079364937
median-0.008871051205
Q30.1296813862
95-th percentile1.354202507
Maximum61.27477134
Range173.6242663
Interquartile range (IQR)0.3376178799

Descriptive statistics

Standard deviation7.135246463
Coefficient of variation (CV)-146.0032397
Kurtosis177.650018
Mean-0.04887046669
Median Absolute Deviation (MAD)0.1629008196
Skewness-7.917883647
Sum-18.81512967
Variance50.91174208
MonotocityNot monotonic
2021-04-07T17:11:02.903568image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.29088492321
 
0.3%
-0.43814041751
 
0.3%
0.0043688593421
 
0.3%
0.075592536821
 
0.3%
-0.071357235711
 
0.3%
1.6884603211
 
0.3%
-4.562969741
 
0.3%
-0.68800706841
 
0.3%
0.78578648321
 
0.3%
0.041584614471
 
0.3%
Other values (375)375
97.4%
ValueCountFrequency (%)
-112.34949491
0.3%
-14.812006821
0.3%
-7.8799384981
0.3%
-7.1820322251
0.3%
-6.2875588641
0.3%
ValueCountFrequency (%)
61.274771341
0.3%
43.844161581
0.3%
13.152586691
0.3%
12.241948851
0.3%
12.202830191
0.3%

LRm_3_pct_delta
Real number (ℝ)

Distinct378
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.6073273619
Minimum-44.99899866
Maximum99.00247961
Zeros1
Zeros (%)0.3%
Memory size3.1 KiB
2021-04-07T17:11:03.186590image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-44.99899866
5-th percentile-7.209487371
Q1-1.901960454
median-1.113208192
Q30.02777844005
95-th percentile4.938569777
Maximum99.00247961
Range144.0014783
Interquartile range (IQR)1.929738894

Descriptive statistics

Standard deviation7.823576541
Coefficient of variation (CV)-12.88197607
Kurtosis75.50716902
Mean-0.6073273619
Median Absolute Deviation (MAD)0.9544789706
Skewness5.625181019
Sum-233.8210343
Variance61.2083499
MonotocityNot monotonic
2021-04-07T17:11:03.468611image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-13
 
0.8%
-7.3332909482
 
0.5%
-22
 
0.5%
-2.6666666672
 
0.5%
-1.7142866872
 
0.5%
-0.49998807912
 
0.5%
-0.82051231891
 
0.3%
-0.71428585331
 
0.3%
-1.2428569771
 
0.3%
3.7143119891
 
0.3%
Other values (368)368
95.6%
ValueCountFrequency (%)
-44.998998661
0.3%
-28.500023841
0.3%
-27.00061991
0.3%
-24.999475491
0.3%
-16.999650331
0.3%
ValueCountFrequency (%)
99.002479611
0.3%
45.001144441
0.3%
35.999141711
0.3%
24.333566461
0.3%
21.333534671
0.3%

PL
Categorical

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
1.0
199 
0.0
186 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1155
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row0.0
4th row1.0
5th row0.0
ValueCountFrequency (%)
1.0199
51.7%
0.0186
48.3%
2021-04-07T17:11:04.020655image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-04-07T17:11:04.381680image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0199
51.7%
0.0186
48.3%

Most occurring characters

ValueCountFrequency (%)
0571
49.4%
.385
33.3%
1199
 
17.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number770
66.7%
Other Punctuation385
33.3%

Most frequent character per category

ValueCountFrequency (%)
0571
74.2%
1199
 
25.8%
ValueCountFrequency (%)
.385
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1155
100.0%

Most frequent character per script

ValueCountFrequency (%)
0571
49.4%
.385
33.3%
1199
 
17.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII1155
100.0%

Most frequent character per block

ValueCountFrequency (%)
0571
49.4%
.385
33.3%
1199
 
17.2%

Interactions

2021-04-07T17:10:24.915262image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:25.372298image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:25.673320image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:25.987347image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:26.412379image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:26.683397image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:26.949417image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:27.194440image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:27.474458image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:27.723476image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:27.992496image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:28.243515image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:28.482534image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:28.743555image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:29.010575image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:29.284595image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:29.568617image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:29.829638image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:30.098658image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:30.361677image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:30.628698image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:30.858716image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:31.092734image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:31.343752image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:31.599771image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:31.847792image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:32.092812image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:32.329827image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:32.562847image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:32.940874image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:33.196893image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:33.451913image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:33.714934image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:33.975955image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:34.231974image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:34.501993image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:34.775015image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:35.011032image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:35.281053image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:35.554077image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:35.817093image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:36.061112image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:36.309132image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:36.556152image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:36.802169image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:37.060189image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:37.308207image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:37.559226image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:37.809245image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:38.058266image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:38.296282image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:38.558303image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:38.837327image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:39.237761image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:39.493780image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:39.748799image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:40.017822image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:40.287841image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:40.557861image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:40.811883image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:41.073900image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:41.327920image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:41.603945image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:41.849959image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:42.115979image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:42.364001image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:42.632021image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:42.885040image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:43.146060image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:43.412078image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:43.683099image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:43.919118image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:44.183140image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:44.412156image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:44.664175image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:44.899195image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:45.140212image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:45.552243image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:45.786260image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:46.028282image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:46.271299image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:46.508316image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:46.762336image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:46.999354image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:47.268375image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:47.523392image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:47.787414image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:48.036433image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:48.274453image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:48.531472image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:48.792491image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:49.030509image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:49.282527image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:49.525548image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:49.781565image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:50.034585image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:50.289605image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:50.545623image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:50.806644image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:51.054665image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:51.314685image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:51.570703image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:51.986734image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:52.229756image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:52.487772image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:52.754794image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:53.016813image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:53.275832image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:53.525853image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-07T17:10:53.779872image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Correlations

2021-04-07T17:11:04.615698image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-04-07T17:11:04.976716image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-04-07T17:11:05.337745image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-04-07T17:11:05.704774image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-04-07T17:11:06.041798image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-04-07T17:10:54.151899image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
A simple visualization of nullity by column.
2021-04-07T17:10:54.527935image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

closeRSI_14INC_2ROC_2PSL_3CDL_DOJI_3_0.1TRUERANGE_1Z_30ratio_M50M180ratio_M5M20ratio_M20M50ratio_MACDh_12_26_9obv_pct_deltaLRm_3_pct_deltaPL
011.9650.25738210.67340066.6666670.00.380000-0.0423230.9950962.5725801.5244141.271271-0.057871-1.3478261.0
14.2745.38995110.94562633.3333330.00.210000-0.3723190.9809970.1973690.9571020.980236-0.202881-2.3333231.0
25.0766.292947122.76029133.3333330.00.3300003.1521800.9643388.4871810.5537273.6077951.062556-7.7142930.0
311.7445.97584814.915105100.0000000.00.450001-0.7380941.0182490.8998901.1553230.437141-0.186667-3.4999981.0
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54.1547.76738914.01002766.6666670.00.360000-0.1330200.9797712.3333340.8797911.827681-0.029143-2.1428571.0
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913.2150.3823110-0.22658433.3333330.00.250000-0.0557390.8265291.0201510.9476250.9310000.060918-1.5999920.0

Last rows

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3755.5145.2243620-0.36166333.3333330.00.34-0.8066200.9716160.3770491.0738590.823952-0.117321-0.8947370.0
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3773.8830.17835417.47923166.6666670.00.48-0.9702601.1098450.7514881.01731610.695104-0.0115184.4000101.0
3786.0861.57179517.80142066.6666670.00.591.3857470.9980860.7296421.2850520.8188350.595911-2.3749991.0
3795.5069.32987818.695653100.0000000.00.332.4112300.9208751.0324538.1018981.2184671.531729-44.9989991.0
3807.4749.3029580-8.3435570.0000000.00.640.1351631.0769170.4907350.987237-3.682537-0.2070310.4468070.0
38112.6159.41771810.15885266.6666670.00.151.5081210.9583071.8901091.2855721.245400-0.108542-0.9636371.0
38211.9241.73200611.27442366.6666670.00.34-1.0655510.5153460.7325641.9455350.335526-0.165111-0.6250010.0
3835.9558.6219330-2.13816066.6666670.00.251.1151770.9739910.7023811.0774780.5760280.047489-1.2954551.0
3845.6848.9449860-1.73011066.6666670.00.36-0.0124260.9716142.4603180.8909011.6843410.026729-1.3448280.0